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January 29, 2014

Strategic renewal includes the process, content, and outcome of refreshment or replacement of attributes of an organization that have the potential to substantially affect its long-term prospects. (Agarwal and Helfat, 2009)

Four tests for deciding whether your company is ripe for strategic renewal:
1. Your profits are dominated by maturing businesses in which you see limited opportunities for growth.
2. There is a direct threat to your core source of profits.
3. An attractive opportunity is outside your core markets.
4. New ways of making money by some firms are a threat to your core source of profits.

Product and Process Innovation
A System Dynamics-Based Analysis of the Interdependencies
Peter M. Milling and Joachim Stumpfe
2000 Conference Papers

In addition to numerous interactions with the environment the complexity of innovation processes in industrial
companies results from interactions between product and process innovation. An effective innovation management has to take these interdependencies into account coming to a congruent implementation of the different types of innovation.

The U.S. industrial and building sectors could extend the use of their still-productive
assets while improving energy efficiency by investing in technologies that make it easier to
manage energy use. These include variable-speed drives, motor-control systems, high-efficiency
motors and building-systems automation, among others.

January 9, 2014

Psephology and forecasting election results based on opinion polls is well established. But is there a data driven election analysis? The question came to mind as Goldman Sachs came out with a report on Indian Elections 2014 and the Congress Party central government minister were very angry and said why stock broking firm is making election forecasts. Can a security analyst use methods similar to the ones he uses to make stock market target price predictions and price trends. Such methods will be called data-based methods.

Fundamental Analysis and Models for Forecasting Elections

Fundamental models for forecasting elections are models that can make forecasts of the results of elections using only economic and political data available months before the election.

These models provide accurate forecasts of the results of elections before polls on voting intentions can accurately forecast elections. Fundamental models are also useful in that they give a better sense of what factors are driving the outcomes behind elections by indicating which types of economic and political data most meaningfully correlate with election outcomes.

Literature on Forecasting Elections

Arrow et al. (2008) notes that prediction markets resulted in an average forecasted error of 1.5 percentage points in the national popular vote in recent U.S. presidential elections, while the final Gallup poll resulted in an average error of 2.1 percentage points, and Berg et al. (2008a) finds similar results on the average accuracy of prediction markets and polls for forecasting the national popular vote just before U.S. presidential elections.

Holbrook and DeSart (1999), Kaplan and Barnett (2003), and Soumbatiants et al. (2006) show that one can use polls taken just before an election to make fairly accurate forecasts of the state-level results in U.S. presidential elections, and Rothschild (2009) shows this for both polls and prediction markets.

While polls and prediction markets can both be used to make reasonably accurate forecasts
of election results just before an election, these methods are much less reliable when used months
before an election.

Gelman and King (1993) notes that polls for the national popular vote in U.S. presidential elections tend to oscillate wildly in the months before the election takes place, and as a result, polls can be highly unreliable indicators of the election outcomes months before the election.

Arrow et al. (2008) further notes that using prediction market data to make forecasts of the national
popular vote five months before a U.S. presidential election would have resulted in an average error
of over 5 percentage points in recent elections, and that using pre-election polls would have resulted
in even less accurate predictions.

Rothschild (2009) investigates the errors in both polls and prediction markets at forecasting probabilities of victory at the state level up to 130 days before the election, and notes that there is not enough liquidity to even have predictions for some states.

Researchers have investigated many unique methods of forecasting election results months
before the election that may hold more promise than using polls or prediction markets. Several
techniques have been explored for forecasting the results of elections such as using biographical
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See, for example, Arrow et al. 2008, Berg et al. 2008a; 2008b, Brown and Chappell 1999, Holbrook and DeSart 1999, Kaplan and Barnett 2003, Pickup and Johnston 2008, and Soumbatiants et al. 2006. Erikson and Wlezien (2008a) indicates that the inaccuracy of polls suggests a need to systematically adjust for biases
in polls.
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information (Armstrong and Graefe 2011), using measures of how well candidates would be
expected to handle particular issues (Graefe and Armstrong 2012), surveying experts or voters for
their predictions (Jones et al. 2007; Lewis-Beck and Tien 1999), using indices that reflect a variety
concerns such as whether there has been a major policy change, a major military failure or success,
and social unrest or a scandal (Armstrong and Cuzán 2006; Lichtman 2008), or even using pictures
or silent video clips of candidates (Armstrong et al. 2010; Benjamin and Shapiro 2009).

However, while there is an extensive literature on forecasting elections using econometric models, so far the vast majority of this literature has focused on forecasting nationwide results. This holds for forecasting models of presidential elections, which typically focus on forecasting the national popular vote, and for forecasting models of congressional elections, which typically focus on forecasting the number of seats won by each major party in the two branches of Congress.

The focus on forecasting nationwide results for these elections is somewhat unsatisfying
because the results of these elections are typically determined at the state or local level. For
instance, the Electoral College elects the U.S. president, where each state has electors that equal its
congressional representation. Further, gubernatorial and senatorial elections are also state-level
elections. Despite this fact, so far only a handful of papers have addressed questions related to
forecasting election outcomes at the state level using econometric methods. Several papers related
to forecasting the results of the U.S. presidential election at the state level are of limited practical
use for forecasting elections because they focus on showing theoretically how one might make
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See, for example, Abramowitz 2008, Alesina et al. 1996, Bartels and Zaller 2001, Campbell 2008, Cuzán and Bundrick 2008, Erikson and Wlezien 2008, Fair 2009, Haynes and Stone 2004, Hibbs 2008, Holbrook 2008, Lewis-Beck and Tien 2008, Lockerbie 2008, Norpoth 2008, and Sidman et al. 2008.
5 See, for example, Abramowitz 2010, Abramowitz and Segal 1986, Bafumi et al. 2010a, Campbell 2010, Coleman 1997, Cuzán 2010, Fair 2009, Kastellec et al. 2008, Lewis-Beck and Rice 1984; 1985, Lewis-Beck and Tien 2010, and Marra and Ostrom 1989
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forecasts of elections if certain data that is only available after elections were available before the
election (Rosenstone 1983; Holbrook 1991; Strumpf and Phillipe 1999).

Campbell (1992) and Campbell et al. (2006) illustrate how one can combine the results of
polls taken roughly two months before the election with a variety of other economic and political
indicators to make forecasts of the results of U.S. presidential elections at the state level.

Like Campbell (1992) and Campbell et al. (2006), Klarner (2008) uses information from pre-election polls on voting intentions in his forecasting model, though Klarner (2008) uses polls on voting intentions that are taken further in advance of the election than those considered by Campbell (1992) and Campbell et al. (2006). Klarner (2008) does not report the results of the errors in his out-of-sample forecasts in his paper.

Bardwell and Lewis-Beck (2004) report results of a model that forecasts the results of Senate elections in Maine, and Klarner (2008) develops a model that forecasts the results of Senate elections for all states using information from pre-election polls on voting intentions.

Incumbency: In the case of presidential elections, we expect voters to react differently depending on which party is the incumbent president. For this reason, we include a dummy variable that equals 1 (-1) if a Democrat (Republican) is president. However, since there is empirical evidence that voters are less likely to want to reelect members of the incumbent party if the incumbent party has been in office for multiple terms (Abramowitz 2008), we also include a variable that equals 1 (-1) if the Democrats (Republicans) have been in control of the presidency for at least eight consecutive years and 0 otherwise.
.

There is also empirical evidence that voters vote differently in Senate elections that takes place on a midterm than they do in Senate elections that take place the same year as a presidential election. Busch (1999), Chappell and Suzuki (1993) and Grofman et al. (1998) all suggest that voters are less likely to vote for members of the president’s party in Senate elections during a midterm than they are during a year when there is a presidential election.

Past Election Results: One of the best indicators of how states will vote in the future is differences
in how states voted in previous elections. Thus, in forecasting the Electoral College, we consider
variables that represent the difference between the fraction of the major party vote received by the
Democratic candidate in the state and the fraction of the major party vote received by the
Democratic candidate nationwide, in both of the two previous presidential elections.
While these variables are helpful in forecasting the results of future elections, these variables
can also sometimes give a misleading picture of the ideologies of the states. In some previous
presidential elections, there was a major third party candidate who took substantially more votes
from one major party candidate than another. For this reason, we include variables that address how
the vote shares of major third party candidates in previous elections should affect the forecasts we
make for future presidential elections. In particular, we consider the three different third party
candidates who received more than five percent of the national popular vote when they ran for
office.

State Ideology: In addition to past election results, we also find it helpful to include other measures
of ideology.

Different dummy variables for the different types of job experience that a candidate may have had has not been used before in the literature. The one other model for forecasting Senate elections at the state level that includes biographical information about the candidates instead considers a single variable that may assume
any one of several different arbitrarily chosen values depending on the previous experience of the
candidates (Klarner 2008).

While past election results are significant and meaningful in the presidential and senatorial
forecasting models, they are not statistically significant in the gubernatorial model. Including a
variable for past presidential elections analogous to that considered in the presidential and senatorial
models is not statistically significant in the gubernatorial model, perhaps a reflection of the fact that
gubernatorial elections and presidential elections involve different issues and voting patterns in one
of these types of elections are not especially predictive of voting patterns in the other. In addition,

The changes in income are better predictors of election results than gauges of the absolute level of performance of the economy such as, for example, absolute levels of
unemployment.

What are your objectives and plans for the future? The objectives can be ranked in the order of worthiness. They can also be ranked in the order your confidence regarding achieving them. What is confidence? Rosabeth Moss Kanter, Professor of Harvard Business School explains confidence as an expectation of positive outcome based on your resources and abilities. Even though confidence is based your resources and abilities, it is not a personality trait which means you are always confident or diffident..Confidence for each objective is based on an assessment of the situation or the environment. So confidence is an assessment whether the environment is sufficiently fertile for your effort to give you the success with the resources and abilities that you have. If you assessment makes you confident of success, that will spark motivation.

What is motivation. Motivation is an internal urge or force that make you put in the effort, to invest your time and resources, and to persist in sticking to the plan till the last minute in case of competitive events. In case of non competitive activities a confident man will persist till he reaches the goal. An example of non-competitive activity is learning a skill like car driving. It is not confidence or positive thinking itself that produces success; it is the investment of resources and the effort. But if enough confidence is not there, it’s too easy to give up prematurely or not get started at all. Hopelessness and despair prevent action from taking place or even though action starts, it will not be done properly with enthusiasm.

Rosabeth Kanter identified certain barriers to confidence building. Individuals and team leaders have to consider them and eliminate or minimize these barriers to go on and achieve their objectives.

Barriers to Confidence Development. These barriers may appear at the starting of an activity or during the course of an activity.

Self-defeating assumptions.
If you start with a list of weak points and then highlight them in doing your analysis, the likely conclusion will dampen your confidence. It is important to write strong points that will give success first.

Goals that are too big or too distant.
The goals that look feasible with a focused effort and available resources and result in quick successes will build confidence. The goals that are remote and very big will not inspire the same confidence. That is why many times, social organizations are small dedicated teams when they are highlighting big and distant goals, but whenever a short term feasible goal appears, their following increases many times. After that goal is reached once again the popular enthusiasm subsides and the team becomes small. But the dedicated team is essential for the long time survival of an organization.

Declaring victory too soon
Small successes are important but don't declare victory till the real victory is achieved. Celebrate small successes, but inform the audience the further steps needed for complete victory and explain the plan in the context of the success being celebrated.

Do-it-yourself-ing or Promoting stars and Belittling many
In a team activity, don't get into the act of doing and winning all by yourself. No team game is won by a single person. Even though some games are won by the superb performance of an individual, the general confidence of the team is low. It is important to train the team members to be a high performance team. Creating a culture in which everyone is more likely to succeed, through recognizing their strengths and further mentoring them to use those strengths in the anticipated challenges is important. Giving recognition and support to others boosts happiness and self-esteem. Numerous research studies support this hypothesis.A supportive environment makes it easier for teams to succeed in very challenging situations. In industry, Toyota successfully developed a supportive environment where neighbors support each other in meeting quantity as well as quality targets.

Blaming someone else
Failures are inevitable events in any project. An organization which takes failure as a defective output and looks inwards to take corrective actions will develop confidence. An organization whose leaders do not take responsibility for the failure and look inwards but blame others will lose confidence.

Defensiveness
Defensiveness is starting a project with an apology. Who asked you for it? Start any project clearly explaining the worth of it. Start a project based on cost-benefit analysis. Only when critics emerge, you can answer and bring out limitations under which you preferred the project.

Neglecting to anticipate setbacks
Do scenario planning with your team. Give opportunity to team members to point out what can go wrong. Then outline your contingency planning. Keep contingency resources and inform the team about it. Whenever some setbacks are anticipated and planned, the actual happening of the setback will not demoralise the team but makes them put extra effort to tide over the setback.

Over-confidence
Overconfidence is described as arrogance by Prof Kanter. When the team management becomes arrogant, it neglects its team members. When a business team becomes arrogant it neglects its customers. The early signs of failure are ignored and the corrective action is not taken in time. What gives success?

Remember, it is not confidence. It is your work. Overconfidence kills your motive to work. Confidence provides motive to work and succeed.

Hear the Athletic Director and head coach of the Varsity Soccer team at Ryerson University, Dr. Ivan Joseph stating some of the above points in his talk on being confident and becoming confident
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India 2014 Lok Sabha Elections. Which party worker can be more confident?

Success in Delhi Elections. Came Second. But formed the government.
Many reputed persons new to politics are joining the party.
Some members including former MPs and MLAs are joining the party.
Lakhs of young people are joining.
For Delhi, they could collect the target campaign fund of Rs. 20 Cr.
For Lok Sabha their target is one crore (10 million) for lok sabha seat.

BJP
Stupendous success in two state assemblies.
Success in one more assembly.
Their PM nominee Narendra Modi is still the preferred number one choice in the country (TOI opinion poll results 8 Jan 2014)
Success of their mega rallies.Announcement of 272+ mission
More allies willing to come on board
Even foreign media declaring the likely success of the party in elections to come to power.

Congress
Has 12 state governments.
Has the ability to campaign across the country.
Twice it came back to power at the centre after big defeats.
Money is not a problem for election campaigning.

January 2, 2014

Advertising is a pure form of selling cost. This chapter illustrates the general approach of managerial economics to various kinds of marketing or selling outlays.

Nature of Advertising Costs

Advertising costs are designed to increase the demand for the firm's products. Advertising expenditures shift the demand curve to the right of where it would otherwise be.

Selling costs are incurred to the get the business. Production and distribution costs create the product and take it to the market.

Pure selling costs are designed to shift the demand schedule, i.e., to obtain sales that would not otherwise have been obtained at the same price. Selling expenses have no functional relationship to production output. They are a cause of sales or some part of sales. In the short run, sales and hence profit depend on the combinations of price, product improvement or specified functionality and quality, advertising outlay and other selling activities. These four influences are interactive. Advertising can shift the demand curve to the right and also can make demand less elastic.

Promotional Elasticity of Demand

Promotional elasticity of demand measures the responsiveness of sales to changes in the amount of advertising with constant price. Like other elasticity measures, it is the ratio of proportionate chagne in sales to the proportionate change in the advertising that causes the change.

Long-Run Aspect of Advertising

Advertising has long-run impacts apart from shifting the short term demand to the right. It helps a firm to attain strategic advantage in market position and gives it a security that contributes to the long-run profit maximization.

The further discussion in the chapter is focused on three issues.

1. The level of the total advertising expenditure over a period of years.
2. Fluctuations in the annual outlays over the course of a business cycle.
3. Measuring the effects of advertising for planning and control purposes.

1. The Level of Advertising Expenditure

The theory of monopolistic competition provides an opportunity to analyze advertising expenditure and its effect on profit for a monopolist.

Simple theory of behavior of advertising expenditure and its impact: Ad expenditure includes all pure selling costs in the theory. The marginal cost of advertising comes down initially and then it goes up in the short run. A firm can go on advertising till the marginal cost of advertising becomes equal to gross profit (gross profit assumed to be constant).

Methods for determining total advertising budget: While the theoretical rule of marginal cost is equal to gross profit on a unit is rational, firms use more simple thumb rules. They are further discussed in the chapter.

3. Measuring the effects of advertising for planning and control purposes.

One has to measure how much the firm's demand schedule has shifted as a result of a specified amount of advertising outlay. Historical data if available can be studied in two ways. (i) by comparing firms with different advertising outlays and (ii) by comparing a firm's performance over time with different advertising outlays.

While Joel Dean's book authored in 1951 has pointed out that economics of advertising is an important managerial economics issue for top management, it has not provided much content. One has to see the contribution of subsequent authors in this topic to get more useful direction.